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\title{Machine Learning for Building Intelligent Recommender Systems}
\author{Weinan Zhang}
\date{April, 2012}
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\section{Introduction}
Currently, I am a research assistant in Shanghai Jiao Tong University, China. My research interests include Machine Learning and its applications in recommender systems, Web mining, and Internet advertising. In the recent year, I become much interested in \emph{collaborative filtering} approaches to recommender systems.

Collaborative filtering works by collecting the preference data of a large number of users on the items, and make recommendations to each user based on the preference patterns of other users. Its basic assumption is that a user would usually be interested in those items preferred by other users with similar interests. Collaborative filtering originated during last decade and had several research tides due to some relevant international competitions, such as Netflix Prize (2006-2009) \cite{bennett2007netflix}, and Yahoo! Music Recommendation (2011) \cite{kddcup11}. Nowadays, collaborative filtering is still an attractive topic in both academia and industry.

\section{Interested Research Problems}

Regarding CF as a machine learning problem, various learning models have been proposed and used in applications, such as neighborhood-based models (kNN \cite{sarwar2001item}, Clustering \cite{xue2005scalable}) and latent factor models (SVD \cite{koren2009matrix}, SVD++ \cite{koren2008factorization}). Although these models performs well in the experiments and even practical applications, there is much potential to improve, even a subversive development.
In my opinion, currently there are several major problems of CF recommender systems.

\begin{itemize}
\item \textbf{Cold Start.} Item recommendation for a new user or user recommendation for a new item. Generally, content- or attribute-based approaches \cite{pazzani2007content} find the item-item or user-user similarity and then provide recommendations. However, if no content or attribute information available, these approaches are of little use. Another primary approach is interview-based \cite{golbandi2011adaptive,zhou2011functional}. This approach generally uses a decision tree to identify the new user's preference profile by asking as few as possible questions. To the best of my knowledge, there is no similar work for new items, that is, with the original several users' preference information on the new item, recommending it to more users.
\item \textbf{Context-Aware.} Users' interest varies along with context they belong, such as mood, location, and social relationship. How to extract and utilize these kinds of context information becomes a hot problem. The competition and workshop named Context-Aware Movie Recommendation (CAMRa) \cite{Berkovsky:2010:1869652} has been successfully held for two years. On the other hand, if there is no explicit context information available, the user's recent preference behavior can also be used as a context in some scenarios such as music recommendation.
\item \textbf{Multiple Objectives.} Different ranking performance metrics are needed in different recommendation tasks, such as diversity in Amazon item recommendation, serendipity in Pandora music recommendation, and freshness of Netflix movie recommendation. However, most ranking-oriented CF models, such as Bayesian Personalized Ranking \cite{Rendle:UAI09}, is not ready to directly optimize these metrics. Postprocessing-based approaches perform a reordering or filtering to the traditional recommended item list. In my opinion, some information retrieval approaches \cite{agrawal2009diversifying,quoc2007learning} can be adopted to related CF tasks.
\item \textbf{Large Scale.} Netflix and Yahoo! Music are two largest datasets for CF. In a large-scale dataset, besides the numerous users and items, various attributes are assigned with each user (such as gender, age, and occupation) and item (such as taxonomy, artist, producer). In my picture, light-weight algorithms and distributed systems are two solutions for large-scale data. YouTube uses a neighborhood propagation algorithm \cite{Davidson:2010:YVR:1864708.1864770} to make video recommendation. Gemulla et al. \cite{Gemulla:KDD11} propose to distribute stochastic gradient descent for matrix factorization. However, it is difficult to impose auxiliary information into this model.
\end{itemize}

These problems are regarded as major problems of recommender systems. And I am interested in finding effective solutions to any of these problems using machine learning and other techniques in information retrieval, data mining, and semantic networks.

\section{Research Experience}
I have a 3-year research career since I entered ApexLab in the summer of 2009. In the machine learning \& data mining group of ApexLab, my supervisors are Professor Gui-Rong Xue and Professor Yong Yu. From the summer of 2010 to the winter of 2011, I was a research intern in Internet advertising team of Microsoft Research Asia, supervised by Tie-Yan Liu and Bin Gao. In addition, during the summer of 2011, I was a visiting scholar in DERI, Ireland. The topics of my research work are listed as follows.

\begin{itemize}
\item {\bf Collaborative Filtering} \hfill {\bf Feb. 2011 - Now}\\
Currently I am the leader of recommender system group in ApexLab. The research topics include informative collaborative filtering, distributed matrix factorization, and transfer learning on collaborative filtering. In KDD-Cup 2011 Yahoo! music recommendation contest, my team won the third place of track 1, and best single-model performance. After this competition, we focus on the research about context-aware CF models. Recently, four papers are submitted to RecSys 2012.

\item {\bf Sponsored Search} \hfill {\bf Jun. 2010 - Jan. 2011}\\
Research topics about advertiser-oriented service in sponsored search. Deep analysis of search advertising log and large-scale non-linear optimization are the main parts of my work. These works were developed when I was a research intern in Microsoft Research Asia. Two papers are submitted to TKDE and SigKDD 2012 respectively.

\item {\bf Contextual Advertising} \hfill {\bf Oct. 2009 - Sep. 2011}\\
I have two research works about contextual advertising using semantic knowledge bases. The paper about advertising keywords recommendation on short-text Web pages has been published on ACM TIST.

\item {\bf Learning to Rank} \hfill {\bf Mar. 2010 - May. 2010}\\
I participated the Yahoo! Learning To Rank Challenge in 2010. Models include RankSVM, RankNet, LambdaRank, and GBDT. Despite of no good ranking, I learned a lot and became familiar with these models. Now I start to research collaborative filtering leveraged by learning to rank models.

\item {\bf Text Classification}  \hfill {\bf Jul. 2009 - Mar. 2010}\\
I participated the international contest LSHTC about large scale hierarchical text classification on ODP taxonomy. The main algorithm was a two-stage deep classifier. Final rank: 5, 1, 3 and 4th in four tasks of the contest. Unfortunately, I did not know the ensemble algorithms at that time, or I think better performance would be obtained.

\end{itemize}

\section{Summary}
There is a long way to go for intelligent recommender systems. For this goal, I am interested in designing the various approaches, especially using machine learning and related techniques.
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